PROJECT ABSTRACT Salmonella (non-typhi) causes 1.2M illnesses and 23K hospitalizations yearly; 100K of these infections are antibiotic-resistant. The steady emergence of antibiotic resistance suggests that Salmonella are actively evolving to evade antibiotics, but our knowledge of how Salmonella become resistant to certain antibiotics is extremely limited. Here we propose to elucidate how Salmonella acquires antibiotic resistance, as a critical first step in developing effective methods to prevent and treat this prevalent disease. Salmonella is an ideal pathogen to study for two reasons: first, Salmonella is highly experimentally tractable in vitro; second, findings from studying Salmonella are likely applicable to other closely related Gram-negative enteric pathogens (Shigella, Escherichia coli, etc.). Leveraging a committed and experienced multidisciplinary team of experts in microbial genomics tool development (Bhatt) and Salmonella biology (Monack), and supported by experts in statistical genetics, clinical microbiology, antibiotic resistance, adaptive laboratory evolution and emerging bacterial pathogens, we will identify and validate new Salmonella genes that are critical for resistance to the three most important antibiotics used to treat Salmonella: ceftriaxone, ciprofloxacin, and azithromycin. To achieve this goal, we have (1) collected a large set of sequenced pathogen isolates and matched phenotype data, and (2) developed and validated a computational method to comprehensively identify mutations associated with antibiotic resistance. Specifically, we will use existing adaptive laboratory evolution data from experiments on Gram-negative enteric pathogens; we will also access a unique CDC dataset of 1,579 Salmonella strains and antibiotic sensitivity phenotypes. Methodologically, we have developed and validated an innovative new computational tool, called mustache, which identifies a prevalent but often overlooked class of mutations using sequencing data. Mustache identifies the location of insertion sequences/transposases, or so-called ?jumping genes?, using short-read DNA sequencing information. When combined with existing high-throughput genomic analysis methods to identify point mutations and small insertions/deletions, our tool generates a comprehensive list of mutations associated with antibiotic resistance. By applying our tool to data from existing adaptive laboratory evolution experiments (Aim 1) and the CDC Salmonella dataset (Aim 2), we anticipate identifying many novel genes that are associated with antibiotic resistance. We will also perform an orthogonal, high-throughput antibiotic resistance screen in two transposon-mutagenized libraries of Salmonella to identify antibiotic-resistance genes in vitro (Aim 3). We anticipate that the results from all three aims will converge upon important and exciting new antibiotic targets in Salmonella as well as enteric Gram-negative pathogens, more broadly. These results will inform the development of novel targeting strategies for an entire class of pathogens in the ?serious threat? category, including antibiotic resistant Salmonella, Shigella, and beyond.